Individualised electrophysiological neural field models for the assessment of thalamocortical function in disorders of consciousness: A multicentre study

Understanding the neural mechanisms underlying disorders of consciousness (DoC) remains a major challenge, particularly in distinguishing limited awareness in minimally conscious state (MCS) and complete unawareness in unresponsive wakefulness syndrome, also coined vegetative state (UWS/VS). In this multicentre study, we fitted a biophysically informed corticothalamic neural field model to high-density EEG data from two large independent datasets, comprising 203 UWS patients, 270 MCS patients and 74 healthy controls. We then used the fitted parameters to simulate EEG time series on a per-subject basis and compared empirical and simulated complexity metrics. The model reliably captured the spectral features across different states of consciousness and revealed reduced corticothalamic integrity in DoC patients that was more pronounced in UWS than in MCS, supporting the mesocircuit hypothesis. Furthermore, the simulated EEG reproduced the complexity patterns of the empirical recordings, with permutation entropy emerging as a sensitive marker capable of distinguishing between MCS and UWS for both real and simulated time series.Author summary: It is a major challenge in neuroscience and clinical care to understand why some patients show signs of minimal consciousness after severe brain injury, while others remain unaware. In this study, we used a physiologically informed corticothalamic model that describes how different neural populations fire and communicate to generate the electrical signals measured by EEG. By fitting the model to recordings of two large independent datasets of patients and healthy participants, we found that it could reliably reproduce the individual brain activity of different groups. The model parameters that describe the communication between the cortex and thalamus populations were found to be different between patient groups. Furthermore, the model was also able to reproduce important patterns of complexity of the real recordings, as the complexity measures obtained from the simulations were found to be highly correlated with the empirical ones and differed between patients who showed minimal consciousness and patients with unresponsive wakefulness. Together, these findings offer new insight into the brain mechanisms that support consciousness after injury and demonstrate the potential of biophysical modelling to create personalised models for future therapeutic applications.